Faster R-CNN [1]} uses the VOC dataset for training. Most of the object classes in the VOC dataset [2]} are rare in urban or indoor settings, such as cows, horses, sheeps, airplanes, and boats.
Our work trained a faster R-CNN model on the ImageNet 2014 dataset [3]}, which contain categories that are more relevant to indoor/urban settings, including cars, motorcycles, bicycles, traffic lights, televisions, chairs, flowerpots, cups, and keyboards.
Note that this framework can be easily modified to parse out any other subset of classes from the ImageNet dataset that are relevant to the specific applications.
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